U.S. patent number 8,572,007 [Application Number 12/916,267] was granted by the patent office on 2013-10-29 for systems and methods for classifying unknown files/spam based on a user actions, a file's prevalence within a user community, and a predetermined prevalence threshold.
This patent grant is currently assigned to Symantec Corporation. The grantee listed for this patent is Kent Griffin, Pratyusa Manadhata, Zulfikar Ramzan. Invention is credited to Kent Griffin, Pratyusa Manadhata, Zulfikar Ramzan.
United States Patent |
8,572,007 |
Manadhata , et al. |
October 29, 2013 |
**Please see images for:
( Certificate of Correction ) ** |
Systems and methods for classifying unknown files/spam based on a
user actions, a file's prevalence within a user community, and a
predetermined prevalence threshold
Abstract
A computer-implemented, server-side method for classifying
unknown files based on user actions may include (1) identifying at
least one file whose trustworthiness is unknown, (2) identifying a
report received from at least one client device that identifies at
least one action taken by a user within a user community when
informed by security software on the client device that the
trustworthiness of the file is unknown, (3) determining that the
action taken by the user indicates that the user believes the file
is trustworthy, (4) classifying the file as trustworthy based at
least in part on the action taken by the user, and then (5)
providing the file's classification to at least one computing
device in order to enable the computing device to evaluate the
trustworthiness of the file. Corresponding systems, encoded
computer-readable media, and client-side methods are also
disclosed.
Inventors: |
Manadhata; Pratyusa
(Piscataway, NJ), Griffin; Kent (Los Angeles, CA),
Ramzan; Zulfikar (Cupertino, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Manadhata; Pratyusa
Griffin; Kent
Ramzan; Zulfikar |
Piscataway
Los Angeles
Cupertino |
NJ
CA
CA |
US
US
US |
|
|
Assignee: |
Symantec Corporation (Mountain
View, CA)
|
Family
ID: |
49448721 |
Appl.
No.: |
12/916,267 |
Filed: |
October 29, 2010 |
Current U.S.
Class: |
706/12;
726/13 |
Current CPC
Class: |
G06F
21/567 (20130101); G06F 21/56 (20130101); G06F
2221/2115 (20130101) |
Current International
Class: |
G06F
11/00 (20060101) |
Field of
Search: |
;706/12,47,62
;717/174,168,189 ;713/2 ;726/13 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Damiani et al., P2P-Based Collaborative Spam Detectionand
Filtering, 2004, International Conference on Peer-to-Peer
Computing, pp. 1-8. cited by examiner .
Kong et al., Collaborative spam filtering using e-mail networks,
2006, UC Los Angeles, IEEE, pp. 67-73. cited by examiner .
Satish, Sourabh; U.S. Appl. No. 12/049,751, filed Mar. 17, 2008.
cited by applicant .
Satish, Sourabh; U.S. Appl. No. 12/056,379, filed Mar. 27, 2008.
cited by applicant .
Nachenberg, Carey S.; U.S. Appl. No. 12/415,834, filed Mar. 31,
2009. cited by applicant .
Sourabh Satish et al.; Social Trust Based Security Model; Mar. 31,
2006; U.S. Appl. No. 11/394,846. cited by applicant.
|
Primary Examiner: Vincent; David
Attorney, Agent or Firm: ALG Intellectual Property, LLC
Claims
What is claimed is:
1. A computer-implemented method for classifying unknown files
based on user actions, at least a portion of the method being
performed by a server-side computing device comprising at least one
processor, the method comprising: identifying at least one file
whose trustworthiness is unknown due at least in part to the file's
prevalence within a user community being below a predetermined
prevalence threshold, wherein the predetermined prevalence
threshold represents a minimum number of client devices within the
user community that have encountered an instance of the file;
identifying a report received from at least one client device that
identifies at least one action taken by a user within the user
community after being informed by security software on the client
device that the trustworthiness of the file is unknown; determining
that the action taken by the user indicates that the user believes
the file is trustworthy even after being informed by the security
software on the client device that the trustworthiness of the file
is unknown; classifying the file as trustworthy based at least in
part on the action taken by the user; providing the file's
classification to at least one computing device in order to enable
the computing device to evaluate the trustworthiness of the
file.
2. The method of claim 1, wherein identifying the file whose
trustworthiness is unknown comprises: identifying an initial
trustworthiness classification assigned to the file; determining
that a confidence score associated with the initial trustworthiness
classification fails to satisfy a predetermined confidence
threshold.
3. The method of claim 2, wherein determining that the confidence
score associated with the initial trustworthiness classification
fails to satisfy the predetermined confidence threshold comprises
determining that the file's prevalence within the user community
fails to satisfy the predetermined prevalence threshold.
4. The method of claim 1, wherein the action taken by the user
comprises installing the file.
5. The method of claim 1, wherein identifying the report received
from the client device comprises: identifying a plurality of
reports received from a plurality of client devices that identify
actions taken by users within the user community after being
informed by security software on the client devices that the
trustworthiness of the file is unknown; determining that the
actions taken by the users indicate that the users believe the file
is trustworthy even after being informed by the security software
on the client devices that the trustworthiness of the file is
unknown.
6. The method of claim 5, wherein determining that the actions
taken by the users indicate that the users believe the file is
trustworthy comprises: analyzing the actions taken by the users to
identify at least one of: the number of users within the user
community that installed the file; the number of users within the
user community that blocked the file; the number of users within
the user community that quarantined the file; computing an install
score that represents a function of the number of users within the
user community that installed the file relative to the number of
users within the user community that blocked or quarantined the
file; computing a threshold that represents a minimum level of
trustworthiness for the file; determining that the install score
for the file satisfies the threshold.
7. The method of claim 6, wherein computing the threshold
comprises: iterating over a set of values in order to identify a
value that minimizes false positives and maximizes true positives;
selecting the value as the threshold.
8. The method of claim 6, further comprising, prior to computing
the install score, assigning a weight to one or more of the actions
taken by the users in order to increase or decrease the actions'
influence in the computation of the install score.
9. The method of claim 8, wherein the weight comprises at least one
of: a weight associated with a user reputation; a weight that
represents a function of the number of users within the user
community that installed the file relative to the number of users
within the user community that encountered the file.
10. The method of claim 1, further comprising: including the file
within a training corpus; using the training corpus to train, based
at least in part on at least one attribute of the file, a
classification heuristic capable of determining the trustworthiness
of files; deploying the classification heuristic.
11. The method of claim 10, wherein the attribute comprises at
least one of: the number of users within the user community that
installed the file; an install score for the file; an install score
weighted by at least one user reputation; an install score weighted
by the number of users within the user community that installed the
file relative to the number of users within the user community that
encountered the file.
12. The method of claim 1, wherein providing the file's
classification to the computing device comprises at least one of:
providing the file's classification to the client device; providing
the file's classification to at least one additional client
device.
13. A computer-implemented method for identifying files that have
been classified as trustworthy based on user actions, at least a
portion of the method being performed by a client device comprising
at least one processor, the method comprising: identifying a file;
querying a server for a trustworthiness classification assigned to
the file; receiving, from the server, a trustworthiness
classification assigned to the file that indicates that the file is
likely trustworthy, wherein: the trustworthiness classification
assigned to the file by the server is based at least in part on at
least one action taken by a user of at least one additional client
device after being informed by security software on the additional
client device that the trustworthiness of the file is unknown due
at least in part to the file's prevalence within a user community
being below a predetermined prevalence threshold, wherein the
predetermined prevalence threshold represents a minimum number of
client devices within the user community that have encountered an
instance of the file; the action taken by the user indicates that
the user believes the file is trustworthy even after being informed
by the security software on the client device that the
trustworthiness of the file is unknown; allowing the file to
install on the client device.
14. The method of claim 13, wherein the action taken by the user of
the additional client device comprises installing the file on the
additional client device.
15. A system for classifying unknown files based on user actions,
the system comprising: an identification module programmed to:
identify at least one file whose trustworthiness is unknown due at
least in part to the file's prevalence within a user community
being below a predetermined prevalence threshold, wherein the
predetermined prevalence threshold represents a minimum number of
client devices within the user community that have encountered an
instance of the file; identify a report received from at least one
client device that identifies at least one action taken by a user
within the user community after being informed by security software
on the client device that the trustworthiness of the file is
unknown; a classification module programmed to: determine that the
action taken by the user indicates that the user believes the file
is trustworthy even after being informed by the security software
on the client device that the trustworthiness of the file is
unknown; classify the file as trustworthy based at least in part on
the action taken by the user; provide the file's classification to
at least one computing device in order to enable the computing
device to evaluate the trustworthiness of the file; at least one
processor configured to execute the identification module and the
classification module.
16. The system of claim 15, wherein the identification module is
further programmed to: identify an initial trustworthiness
classification assigned to the file; determine that a confidence
score associated with the initial trustworthiness classification
fails to satisfy a predetermined confidence threshold.
17. The system of claim 15, wherein the identification module is
further programmed to: identify an initial trustworthiness
classification assigned to the file; determine that the file's
prevalence within the user community fails to satisfy the
predetermined prevalence threshold.
18. The system of claim 15, wherein the action taken by the user
comprises installing the file.
19. The method of claim 1, wherein: determining that the action
taken by the user indicates that the user believes the file is
trustworthy comprises determining that the action taken by the user
indicates that the user has a personal knowledge of the file's
legitimacy; classifying the file as trustworthy based at least in
part on the action taken by the user comprises classifying the file
as trustworthy due at least in part to the user's personal
knowledge of the file's legitimacy.
20. The method of claim 1, wherein the minimum number of client
devices within the user community that have encountered an instance
of the file comprises a minimum percentage of client devices within
the user community that have encountered an instance of the file.
Description
BACKGROUND
In a reputation-based security system, a security-software vendor
may attempt to determine the trustworthiness of a file by
collecting, aggregating, and analyzing information from potentially
millions of user devices within a community, such as the vendor's
user base. For example, by determining a file's origin, age, and
prevalence within a community, among other details (such as whether
the file is predominantly found on at-risk or "unhealthy" machines
within the community), a security-software vendor may gain a fairly
accurate understanding as to the trustworthiness of the file.
Unfortunately, prior to collecting sufficient information about a
file, reputation-based security systems may be unable to accurately
determine the trustworthiness of the file. As a result, rather than
running the risk of producing a false negative or false positive,
reputation-based security systems may classify the file's
trustworthiness as unknown and allow users to download or install
the file at their own discretion. In this example, upon
encountering a file whose trustworthiness is unknown, some users
within a community may decide to download or install the file based
on a personal knowledge of or belief in the file's (or file
source's) legitimacy.
Although such user actions (e.g., downloading or installing the
file) may provide additional information about the trustworthiness
of the file based on users' personal knowledge, current
reputation-based security systems typically fail to take advantage
of this additional source of information when classifying the
trustworthiness of files. As such, the instant disclosure
identifies a need for systems and methods for classifying unknown
files based at least in part on actions taken by users within a
community.
SUMMARY
As will be described in greater detail below, the instant
disclosure generally relates to systems and methods for classifying
unknown files based on user actions. In one example, a server or
backend may accomplish such a goal by (1) identifying at least one
file whose trustworthiness is unknown, (2) identifying a report
(such as a server ping) received from at least one client device
that identifies at least one action (such as installing the file)
taken by a user within a user community (such as an enterprise or
the user base of a security-software vendor) when informed by
security software on the client device that the trustworthiness of
the file is unknown, (3) determining that the action taken by the
user indicates that the user believes the file is trustworthy, (4)
classifying the file as trustworthy based at least in part on the
action taken by the user, and then (5) providing the file's
classification to at least one computing device (such as the client
device in question or an additional client device) in order to
enable the computing device to evaluate the trustworthiness of the
file.
In one embodiment, the server or backend may identify an initial
trustworthiness classification assigned to the file and a
confidence score associated with the initial trustworthiness
classification. In this embodiment, the server or backend may
determine that the confidence score fails to satisfy a
predetermined threshold. The server or backend may then reclassify
the file's trustworthiness as unknown due to the low confidence
score associated with the initial classification. In some examples,
the confidence score may fail to satisfy the predetermined
threshold because the file's prevalence within the user community
fails to satisfy a predetermined threshold.
In one example, the server or backend may, by collecting,
aggregating, and analyzing reports from users within the user
community that have encountered the file, determine that actions
taken by such users indicate that they believe the file is
trustworthy. The server or backend may accomplish this in a variety
of ways, including by computing an install score that represents a
function of the number of users within the user community that
installed the file relative to the number of users within the user
community that blocked or quarantined the file. In this example,
the server or backend may classify the file as trustworthy upon
determining that the install score for the file satisfies a
threshold that represents a minimum level of trustworthiness for
the file. In one example, the server or backend may determine this
threshold by iterating over a set of values (such as install scores
for various files) and identifying the value that minimizes false
positives and maximizes true positives.
In some examples, the server or backend may assign a weight to one
or more of the user actions in order to increase or decrease the
actions' influence in the computation of the install score. In one
example, the weight may be associated with the reputation of a user
that installed the file. In another example, the weight may
represent a function of the number of users that installed the file
relative to the number of users that encountered the file.
In at least one example, the server or backend may use one or more
attributes of the file (such as the number of users that installed
the file and/or a weighted or non-weighted install score for the
file) to train a classification heuristic capable of determining
the trustworthiness of files. In some examples, the server or
backend may then deploy the classification heuristic in order to
identify trustworthy files within the user community.
In another example, a client device may identify a file that has
been classified as trustworthy based at least in part on user
actions reported in the server-side process outlined above. In this
example, the client device may accomplish such a goal by (1)
identifying a file, (2) querying a server for a trustworthiness
classification assigned to the file, (3) receiving, from the
server, a trustworthiness classification assigned to the file that
indicates that the file is likely trustworthy, and then (4)
allowing the file to install on the client device. As detailed
above, the trustworthiness classification assigned to the file by
the server may be based at least in part on at least one action
(such as installing the file) taken by a user of at least one
additional client device when informed by security software on the
additional client device that the trustworthiness of the file is
unknown. In this example, the action taken by the user may indicate
that the user believes the file is trustworthy
As will be explained in greater detail below, the various systems
and methods described herein may be able to accurately determine
trustworthiness of a file based at least in part on actions taken
by users when informed that the trustworthiness of the file is
unknown. As such, these systems and methods may effectively take
advantage of this additional source of information (i.e., user
actions) in order to successfully identify trustworthy files at an
earlier point in time than is possible in conventional systems
without unduly increasing false-negative rates within a
community.
Features from any of the above-mentioned embodiments may be used in
combination with one another in accordance with the general
principles described herein. These and other embodiments, features,
and advantages will be more fully understood upon reading the
following detailed description in conjunction with the accompanying
drawings and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings illustrate a number of exemplary
embodiments and are a part of the specification. Together with the
following description, these drawings demonstrate and explain
various principles of the instant disclosure.
FIG. 1 is a block diagram of an exemplary system for classifying
unknown files based on user actions.
FIG. 2 is a block diagram of an exemplary system for classifying
unknown files based on user actions.
FIG. 3 is a flow diagram of an exemplary server-side method for
classifying unknown files based on user actions.
FIG. 4 is an illustration of an exemplary report generated by a
client device upon detecting a user action taken on a file whose
trustworthiness is unknown.
FIG. 5 is an illustration of exemplary reputation information that
may be used to determine that a file is trustworthy.
FIG. 6 is a flow diagram of an exemplary client-side method for
identifying files that have been classified as trustworthy based on
user actions.
FIG. 7 is a block diagram of an exemplary computing system capable
of implementing one or more of the embodiments described and/or
illustrated herein.
FIG. 8 is a block diagram of an exemplary computing network capable
of implementing one or more of the embodiments described and/or
illustrated herein.
Throughout the drawings, identical reference characters and
descriptions indicate similar, but not necessarily identical,
elements. While the exemplary embodiments described herein are
susceptible to various modifications and alternative forms,
specific embodiments have been shown by way of example in the
drawings and will be described in detail herein. However, the
exemplary embodiments described herein are not intended to be
limited to the particular forms disclosed. Rather, the instant
disclosure covers all modifications, equivalents, and alternatives
falling within the scope of the appended claims.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
The following will provide, with reference to FIGS. 1-2, detailed
descriptions of exemplary systems for classifying unknown files
based on user actions. Detailed descriptions of corresponding
computer-implemented methods will also be provided in connection
with FIG. 3-6. In addition, detailed descriptions of an exemplary
computing system and network architecture capable of implementing
one or more of the embodiments described herein will be provided in
connection with FIGS. 7 and 8, respectively.
FIG. 1 is a block diagram of an exemplary system 100 for
classifying unknown files based on user actions. As illustrated in
this figure, exemplary system 100 may include one or more modules
102 for performing one or more tasks. For example, and as will be
explained in greater detail below, exemplary system 100 may include
an identification module 104 programmed to (1) identify at least
one file whose trustworthiness is unknown and/or (2) identify a
report received from at least one client that identifies at least
one action taken by a user within a user community when informed by
security software on the client device that the trustworthiness of
the file is unknown.
In addition, and as will be described in greater detail below,
exemplary system 100 may include a classification module 106
programmed to (1) determine that the action taken by the user
indicates that the user believes that the file is trustworthy, (2)
classify the file as trustworthy based at least in part on the
action taken by the user, and then (3) provide the file's
classification to at least one computing device in order to enable
the computing device to evaluate the trustworthiness of the file.
Exemplary system 100 may also include a security module 108
programmed to identify a file that has been classified as
trustworthy based at least in part on user actions reported in the
server-side process outlined above. Although illustrated as
separate elements, one or more of modules 102 in FIG. 1 may
represent portions of a single module or application.
In certain embodiments, one or more of modules 102 in FIG. 1 may
represent one or more software applications or programs that, when
executed by a computing device, may cause the computing device to
perform one or more tasks. For example, and as will be described in
greater detail below, one or more of modules 102 may represent
software modules stored and configured to run on one or more
computing devices, such as the devices illustrated in FIG. 2 (e.g.,
client devices 202(1)-(N) and/or server 206), computing system 710
in FIG. 7, and/or portions of exemplary network architecture 800 in
FIG. 8. One or more of modules 102 in FIG. 1 may also represent all
or portions of one or more special-purpose computers configured to
perform one or more tasks.
As illustrated in FIG. 1, exemplary system 100 may also include one
or more databases, such as database 120. In one example, the
various systems described herein may store reputation information
122 within database 120. As will be explained in greater detail
below, reputation information 122 may represent information that
identifies the reputation or prevalence of one or more files within
a community (such as an enterprise or the user base of a
security-software vendor) and/or the number of times the file has
been installed on computing devices within the community.
Database 120 may represent portions of a single database or
computing device or a plurality of databases or computing devices.
For example, database 120 may represent a portion of server 206 in
FIG. 2, computing system 710 in FIG. 7, and/or portions of
exemplary network architecture 800 in FIG. 8. Alternatively,
database 120 in FIG. 1 may represent one or more physically
separate devices capable of being accessed by a computing device,
such as server 206 and/or client devices 202(1)-(N) in FIG. 2,
computing system 710 in FIG. 7, and/or portions of exemplary
network architecture 800 in FIG. 8.
Exemplary system 100 in FIG. 1 may be deployed in a variety of
ways. For example, all or a portion of exemplary system 100 may
represent portions of exemplary system 200 in FIG. 2. As shown in
FIG. 2, system 200 may include a plurality of client devices
202(1)-(N) in communication with a server 206 via a network
204.
In one embodiment, and as will be described in greater detail
below, identification module 104 and/or classification module 106
may program server 206 to classify unknown files based on user
actions by (1) identifying at least one file whose trustworthiness
is unknown, (2) identifying a report received from at least one
client device (such as one or more of client devices 202(1)-(N))
that identifies at least one action (such as installing the file)
taken by a user within a user community (such as an enterprise or
the user base of a security-software vendor) when informed by
security software on the client device that the trustworthiness of
the file is unknown, (3) determining that the action taken by the
user indicates that the user believes the file is trustworthy, (4)
classifying the file as trustworthy based at least in part on the
action taken by the user, and then (5) providing the file's
classification to at least one computing device (such as the client
device in question or an additional client device) in order to
enable the computing device to evaluate the trustworthiness of the
file.
Similarly, security module 108 may program one or more of client
devices 202(1)-(N) to identify files that have been classified as
trustworthy based at least in part on user actions by (1)
identifying a file, (2) querying a server (such as server 206) for
a trustworthiness classification assigned to the file, (3)
receiving, from the server, a trustworthiness classification
assigned to the file that indicates that the file is likely
trustworthy, and then (4) allowing the file to install on the
client device. As detailed above, the trustworthiness
classification assigned to the file by the server may be based at
least in part on at least one action (such as installing the file)
taken by a user of at least one additional client device when
informed by security software on the additional client device that
the trustworthiness of the file is unknown. In this example, the
action taken by the user may indicate that the user believes the
file is trustworthy.
Client devices 202(1)-(N) generally represent any type or form of
computing device capable of reading computer-executable
instructions. Examples of client devices 202(1)-(N) include,
without limitation, laptops, desktops, servers, cellular phones,
personal digital assistants (PDAs), multimedia players, embedded
systems, combinations of one or more of the same, exemplary
computing system 710 in FIG. 7, or any other suitable computing
device. In one example, client devices 202(1)-(N) may represent
computing devices within a user community (e.g., user community
210), such as an enterprise or the user base of a security-software
vendor.
Server 206 generally represents any type or form of computing
subsystem (such as a reputation service) capable of generating
and/or receiving information that identifies a file's reputation
and/or prevalence as well as any actions taken by users on the
file. As will be explained in greater detail below, in some
examples server 206 may generate such information by collecting,
aggregating, and analyzing data from potentially millions of user
devices within a community (such as an enterprise or user base of a
security-software vendor).
Network 204 generally represents any medium or architecture capable
of facilitating communication or data transfer. Examples of network
204 include, without limitation, an intranet, a wide area network
(WAN), a local area network (LAN), a personal area network (PAN),
the Internet, power line communications (PLC), a cellular network
(e.g., a GSM Network), exemplary network architecture 800 in FIG.
8, or the like. Network 204 may facilitate communication or data
transfer using wireless or wired connections. In one embodiment,
network 204 may facilitate communication between client devices
202(1)-(N) and server 206.
FIG. 3 is a flow diagram of an exemplary computer-implemented,
server-side method 300 for classifying unknown files based on user
actions. The steps shown in FIG. 3 may be performed by any suitable
computer-executable code and/or computing system. In some
embodiments, the steps shown in FIG. 3 may be performed by one or
more of the components of system 100 in FIG. 1, system 200 in FIG.
2, computing system 710 in FIG. 7, and/or portions of exemplary
network architecture 800 in FIG. 8.
As illustrated in FIG. 3, at step 302 the various systems described
herein may identify at least one file (such as an executable or
DLL) whose trustworthiness is unknown. For example, identification
module 104 may, as part of server 206 in FIG. 2, identify an
executable file encountered by at least one client device (e.g.,
client device 202(1)) within user community 210. In this example,
upon identifying the executable file, identification module 104 may
determine that the trustworthiness of the executable file is
unknown due to insufficient information collected by server 206
about the file.
The systems described herein may perform step 302 in a variety of
ways. In one example, identification module 104 may identify a
report (such as a server ping) received from a client device (e.g.,
client device 202(1)) that indicates that the client device has
encountered a file whose trustworthiness is unknown. In this
example, the report may uniquely identify both the file (using,
e.g., the name of the file and/or a hash of the file) and a user
(using, e.g., a user ID) associated with the client.
Additionally or alternatively, identification module 104 may obtain
reputation information for a file from database 120 that indicates
that the file's trustworthiness is unknown. The term "reputation
information," as used herein, generally refers to information that
identifies a file's reputation, attributes, or prevalence within a
community (such as the user base of a security-software vendor).
Reputation information may also include information that identifies
at least one user and/or the user's reputation (e.g., a reputation
score for the user). Examples of reputation information include,
without limitation, reputation scores for files (where, for
example, high reputation scores indicate that a file is generally
trusted within a community and low reputation scores indicate that
a file is generally untrusted within a community), prevalence
information (e.g., information that identifies the number or
percentage of user devices within a community that have encountered
an instance of the file), install information (e.g., information
that identifies the number or percentage of user devices within a
community that installed the file upon encountering an instance of
the file), reputation scores for users (e.g., information that
identifies a reputation for one or more users within a user
community), or any other information that may be used to identify a
community's opinion on the trustworthiness or legitimacy of a file,
such as the file's location, origin, age, etc.
As indicated above, reputation services may generate reputation
information for files by collecting, aggregating, and analyzing
data from user devices within a community. Examples of information
gathered from user devices within a community that may be used to
generate reputation information include, without limitation,
information that identifies the overall health of a user device
(i.e., information that identifies the performance, stability,
and/or state of security of the user device), information that
identifies the files stored on or encountered by a user device,
information that identifies the impact of a file on the health of
the user device (e.g., information that identifies the health of a
user device both before and after a file is encountered by the user
device), and any other information that may be used to evaluate the
trustworthiness of a file. In some examples, by collecting,
aggregating, and analyzing this data from potentially millions of
user devices within a community (such as the user base of a
security-software vendor), reputation services may be able to gain
a fairly accurate understanding as to the trustworthiness of a
file.
By way of illustration, at step 302 identification module 104 may,
as part of server 206 in FIG. 2, identify a report 400 in FIG. 4
received from client device 202(1) that identifies a file
encountered by client device 202(1). In this example, report 400
may include the name of the file ("foo.exe"), a hash of the file
("0xEF9A0349"), and/or the ID of a user associated with client
device 202(1) ("johndoe"), among other information.
In one example, report 400 may indicate that the trustworthiness of
the file in question is unknown. In another example, identification
module 104 may determine that the trustworthiness of the file
identified in report 400 is unknown by obtaining reputation
information 512 in FIG. 5 from database 120 for the file in
question. As illustrated in FIG. 5, reputation information 512 may
contain information that identifies a reputation score associated
with the file in question ("N/A," in this example, meaning
"unknown").
In some examples, the systems described herein may determine that
the trustworthiness of the file is unknown by determining that a
confidence score associated with an initial trustworthiness
classification assigned to the file fails to satisfy a
predetermined threshold. For example, identification module 104 may
determine that the file in question has been classified as
"trustworthy" with a relatively low degree of confidence (e.g.,
"25%") due to insufficient data about the file. In this example,
identification 104 may determine that this low confidence score
fails to satisfy a predetermined threshold (e.g., "75%"), such that
the file must be classified as "unknown."
In some examples, the confidence score associated with the initial
trustworthiness classification may vary based on the prevalence of
the file within user community 210. For example, the confidence
score may increase as the prevalence of the file increases within
user community 210. In this example, identification module 104 may,
in essence, determine that the trustworthiness of the file is
unknown if the prevalence of the file within user community 210
fails to satisfy a predetermined threshold. For example,
identification module 104 may determine that the trustworthiness of
the file is unknown if a predetermined threshold requires at least
50 client devices within user community 210 to have encountered
instances of the file but only 20 client devices within user
community 210 have actually encountered the file.
Returning to FIG. 3, at step 304 the various systems described
herein may identify a report (such as report 400 in FIG. 4)
received from at least one client device that identifies at least
one action taken by a user within a user community when informed by
security software on the client device that the trustworthiness of
the file is unknown. For example, identification module 104 may, as
part of server 206, identify a report received from client device
202(1) that indicates that a user of client device 202(1) decided
to install the file in question despite the file's trustworthiness
being unknown. In this example, client device 202(1) may be
configured to inform the user upon encountering the file that the
file's trustworthiness is unknown.
The systems described herein may perform step 304 in a variety of
ways. In one example, identification module 104 may identify the
report upon receiving the same. In other examples, identification
module 104 may identify the report non-contemporaneously with
respect to receipt of the same. As will be described in greater
detail below in connection with FIG. 6, in some examples client
devices 202(1)-(N) may be configured to send such reports to server
206 either sua sponte (e.g., immediately upon identifying a user
action taken on a file and/or at periodic intervals) and/or at the
request of server 206.
As detailed above, the report identified in step 304 may include a
variety of information, including information that identifies the
name of the file, the hash of the file, the action taken by the
user, a user ID of the user who took the action on the file, the
URL from which the file was downloaded, and the date on which the
file was downloaded. In the example illustrated in FIG. 4,
exemplary report 400 in FIG. 4 may indicate that the user "johndoe"
decided to install the file "foo.exe" even after security software
on client 202(1) informed the user that the trustworthiness of this
file was unknown.
Returning to FIG. 3, at step 306 the various systems described
herein may determine that the action taken by the user indicates
that the user believes the file is trustworthy. For example,
classification module 106 may determine that the user's decision to
install the file on client device 202(1) despite the file's
trustworthiness being unknown indicates that the user has a
personal knowledge of and/or belief in the file's (or file
source's) legitimacy.
The systems described herein may perform step 306 in a variety of
ways. In one example, classification module 106 may operate under
the assumption that a user within user community 210 only installs
a file whose trustworthiness is unknown if the user has a personal
knowledge of and/or belief in the file's (or file source's)
legitimacy. In another example, classification module 106 may only
make such an assumption if the user's reputation satisfies a
predetermined threshold (i.e., if the reputation of the user
indicates that the user generally only installs trusted
software).
In some examples, classification module 106 may aggregate and
analyze reports received from a plurality of client devices within
the community when performing step 306. For example, classification
module 106 may identify, by analyzing reports gathered from a
plurality of client devices, the number of users within user
community 210 that installed the file, the number of users within
user community 210 that blocked the file, and/or the number of
users within user community 210 that quarantined the file. In this
example, classification module 106 may then compute an install
score that represents a function of the number of users within user
community 210 that installed the file relative to the number of
users within the user community that blocked or quarantined the
file. For example, classification module 106 may compute an install
score for the file identified in step 302 by dividing the number of
times the file has been installed by users within user community
210 by the number of times the file has been installed, blocked, or
quarantined (or in other words, the number of times the file has
been encountered) by users within user community 210.
Upon computing the install score for the file, classification
module 106 may determine whether this install score satisfies a
predetermined minimum threshold. If so, then classification module
108 may determine that the overall consensus among users within the
community is that the file is trustworthy. In some examples, the
systems described herein may select this predetermined minimum
threshold by iterating over a set of values in order to identify a
value that minimizes false positives and maximizes true positives
for files encountered within the user community.
In some examples, classification module 106 may assign a weight to
one or more of the actions taken by users within user community 210
in order to increase or decrease the actions' influence in the
computation of the install score. In one example, the respective
weight assigned to each user action may be associated with the
reputation of the user (e.g., a reputation score associated with
the user) who took the action on the file in question. For example,
if the user of client device 202(1) possess a reputation score that
satisfies a predetermined threshold, the action taken by the user
of client device 202(1) may be assigned a weight that increases the
action's influence in the computation of install score for the file
in question.
In another example, the respective weight assigned to each user
action may represent a function of the number of users within user
community 210 that installed the file in question relative to the
number of users within user community 210 that encountered (e.g.,
installed, blocked, or quarantined) the file in question. In this
example, classification module 106 may compute such a function by
dividing a logarithm of the number of users that installed the file
by a logarithm of the number of users that encountered (e.g.,
installed, blocked, or quarantined) the file.
Returning to FIG. 3, at step 308 the various systems described
herein may classify the file as trustworthy based at least in part
on the action taken by the user. For example, classification module
106 may classify the file in question as trustworthy based at least
in part on the user's decision to install the file despite the
file's trustworthiness being unknown at the time that the user made
the decision. In some examples, classification module 106 may
classify the file as trustworthy in response to determining that
the user and/or the user community believes that the file is
trustworthy.
The systems described herein may perform step 308 in a variety of
ways. In one example, classification module 106 may classify the
file by (1) assigning a classification to the file and then (2)
storing this classification within database 120. In another
example, classification module 106 may provide the file's
classification to one or more client devices within user community
210.
Returning to FIG. 3, at step 310 the various systems described
herein may provide the file's classification to at least one
computing device (such as client devices 202(1)-(N)) in order to
enable the computing device to evaluate the trustworthiness of the
file. For example, classification module 106 may receive, from
client device 202(N), a request for the file's classification after
client device 202(N) has encountered an instance of the file in
question. In this example, the file's classification may be
represented by a reputation score (such as a percentage) indicating
that the overall consensus among user community 210 is that the
file is trustworthy.
The systems described herein may perform step 310 in a variety of
ways. In one example, classification module 106 may provide the
file's classification to the client device that originally provided
the report that identified the user action (e.g., client device
202(1)). In another example, classification module 106 may provide
the file's classification to one or more additional client devices,
such as client devices 202(2)-(N). In either example, the client
device may then use this classification in evaluating the
trustworthiness of the file in question, as will be described in
greater detail below in connection with FIG. 6. Upon completion of
step 310, exemplary method 300 in FIG. 3 may terminate.
Although not illustrated in FIG. 3, method 300 may also include one
or more additional steps for classifying unknown files based on
user actions. In one example, classification module 106 may include
the file in question within a training corpus to be used when
training a classification heuristic. The phrase "training corpus,"
as used herein, may refer to a collection of files, file
attributes, items, objects, or other information that may be
analyzed or classified by a classification heuristic.
Examples of training corpus data include, without limitation, files
(such as executables, DLLs, or the like), file attributes (such as
the number of users within the user community that installed a
file, an install score for a file, an install score weighted by at
least one user reputation, an install score weighted by the number
of users within the user community that installed a particular file
relative to the number of users within the user community that
encountered the particular file), or any other item or object that
may be subject to classification or analysis.
In some examples, classification module 106 may use the training
corpus to train a classification heuristic capable of determining
the trustworthiness of files. For example, classification module
106 may customize a classification heuristic to identify file
attributes indicative of malicious files as well as file attributes
indicative of trustworthy files. In this example, the
classification heuristic may then be deployed and applied to a set
of field data (such as files encountered by client devices
202(1)-(N)) in order to detect malware within user community 210.
The phrase "classification heuristic," as used herein, may refer to
any type or form of heuristic, tool, or model capable of
classifying a file as malicious or trustworthy.
As detailed above, client devices may identify files that have been
classified as trustworthy based at least in part on user actions in
accordance with the exemplary method outlined above in connection
with FIG. 3. FIG. 6 is a flow diagram of an exemplary
computer-implemented, client-side method 600 for identifying files
that have been classified as trustworthy based at least in part on
user actions. The steps shown in FIG. 6 may be performed by any
suitable computer-executable code and/or computing system. In some
embodiments, the steps shown in FIG. 6 may be performed by one or
more of the components of system 100 in FIG. 1, system 200 in FIG.
2, computing system 710 in FIG. 7, and/or portions of exemplary
network architecture 800 in FIG. 8.
As illustrated in FIG. 6, at step 602 the systems described herein
may identify a file. For example, security module 108 in FIG. 1
may, as part of client device 202(N) in FIG. 2, identify a file
encountered by client device 202(N).
The systems described herein may identify a file in a variety of
ways. In one example, security module 108 may identify a file upon
encountering the same (whether in an email attachment, a webpage,
or any other suitable entity). In this example, security module 108
may identify the name of the file (e.g., "foo.exe"), a hash of the
file (e.g., "0xEF9A0349"), and/or any other identifying information
associated with the file.
At step 604, the systems described herein may query a server or
backend (e.g., a reputation service) for a trustworthiness
classification assigned to the file identified in step 602. For
example, security module 108 in FIG. 1 may, as part of client
device 202(N) in FIG. 2, query server 206 for a trustworthiness
classification assigned to the file identified in step 602.
In some examples, security module 108 may include a report within
this query that indicates that client device 202(N) has encountered
the file identified in step 602. As detailed above, this report may
also include information that uniquely identifies (using, e.g., a
file name and/or file hash) the file in question.
Returning to FIG. 6, at step 606 the systems described herein may
receive a trustworthiness classification assigned to the file by
the server that indicates that the file in question is likely
trustworthy. For example, security module 108 in FIG. 1 may, as
part of client device 202(N) in FIG. 2, receive a trustworthiness
classification assigned to the file "foo.exe" from server 206 that
indicates that this file is likely trustworthy. In one example, the
trustworthiness classification may be represented by a reputation
score (such as a percentage) that identifies the likelihood of the
file being trustworthy.
As detailed above, this trustworthiness classification may be based
at least in part on at least one action taken by a user of at least
one additional client device (e.g., client device 202(1)) when
informed by security software (such as antivirus software) on the
additional client device that the trustworthiness of the file is
unknown. For example, server 206 may (as explained in greater
detail above in connection with FIG. 3) have assigned this
trustworthiness classification to the file based at least in part
on at least one action (e.g., installing the file) taken by a user
of at least one additional client device within user community 210
that indicates that the user believed the file was trustworthy.
Returning to FIG. 6, at step 608 the systems described herein may
allow the file to install on the client device. For example,
security module 108 in FIG. 1 may, as part of client device 202(N)
in FIG. 2, allow the file identified in step 602 to be installed on
client device 202(N) upon receiving a trustworthiness
classification from server 206 that indicates that the file is
likely trustworthy. Upon completion of step 608, exemplary method
600 in FIG. 6 may terminate.
As explained above, the various systems and methods described
herein may be able to accurately determine trustworthiness of a
file based at least in part on actions taken by users when informed
that the trustworthiness of the file is unknown. As such, these
systems and methods may effectively take advantage of this
additional source of information (i.e., user actions) in order to
successfully identify trustworthy files at an earlier point in time
than is possible in conventional systems without unduly increasing
false-negative rates within a community.
FIG. 7 is a block diagram of an exemplary computing system 710
capable of implementing one or more of the embodiments described
and/or illustrated herein. Computing system 710 broadly represents
any single or multi-processor computing device or system capable of
executing computer-readable instructions. Examples of computing
system 710 include, without limitation, workstations, laptops,
client-side terminals, servers, distributed computing systems,
handheld devices, or any other computing system or device. In its
most basic configuration, computing system 710 may include at least
one processor 714 and a system memory 716.
Processor 714 generally represents any type or form of processing
unit capable of processing data or interpreting and executing
instructions. In certain embodiments, processor 714 may receive
instructions from a software application or module. These
instructions may cause processor 714 to perform the functions of
one or more of the exemplary embodiments described and/or
illustrated herein. For example, processor 714 may perform and/or
be a means for performing, either alone or in combination with
other elements, one or more of the identifying, receiving,
determining, classifying, providing, analyzing, computing,
iterating, selecting, assigning, including, using, training,
querying, and allowing steps described herein. Processor 714 may
also perform and/or be a means for performing any other steps,
methods, or processes described and/or illustrated herein.
System memory 716 generally represents any type or form of volatile
or non-volatile storage device or medium capable of storing data
and/or other computer-readable instructions. Examples of system
memory 716 include, without limitation, random access memory (RAM),
read only memory (ROM), flash memory, or any other suitable memory
device. Although not required, in certain embodiments computing
system 710 may include both a volatile memory unit (such as, for
example, system memory 716) and a non-volatile storage device (such
as, for example, primary storage device 732, as described in detail
below). In one example, one or more of modules 102 from FIG. 1 may
be loaded into system memory 716.
In certain embodiments, exemplary computing system 710 may also
include one or more components or elements in addition to processor
714 and system memory 716. For example, as illustrated in FIG. 7,
computing system 710 may include a memory controller 718, an
Input/Output (I/O) controller 720, and a communication interface
722, each of which may be interconnected via a communication
infrastructure 712. Communication infrastructure 712 generally
represents any type or form of infrastructure capable of
facilitating communication between one or more components of a
computing device. Examples of communication infrastructure 712
include, without limitation, a communication bus (such as an ISA,
PCI, PCIe, or similar bus) and a network.
Memory controller 718 generally represents any type or form of
device capable of handling memory or data or controlling
communication between one or more components of computing system
710. For example, in certain embodiments memory controller 718 may
control communication between processor 714, system memory 716, and
I/O controller 720 via communication infrastructure 712. In certain
embodiments, memory controller 718 may perform and/or be a means
for performing, either alone or in combination with other elements,
one or more of the steps or features described and/or illustrated
herein, such as identifying, receiving, determining, classifying,
providing, analyzing, computing, iterating, selecting, assigning,
including, using, training, querying, and allowing.
I/O controller 720 generally represents any type or form of module
capable of coordinating and/or controlling the input and output
functions of a computing device. For example, in certain
embodiments I/O controller 720 may control or facilitate transfer
of data between one or more elements of computing system 710, such
as processor 714, system memory 716, communication interface 722,
display adapter 726, input interface 730, and storage interface
734. I/O controller 720 may be used, for example, to perform and/or
be a means for performing, either alone or in combination with
other elements, one or more of the identifying, receiving,
determining, classifying, providing, analyzing, computing,
iterating, selecting, assigning, including, using, training,
querying, and allowing steps described herein. I/O controller 720
may also be used to perform and/or be a means for performing other
steps and features set forth in the instant disclosure.
Communication interface 722 broadly represents any type or form of
communication device or adapter capable of facilitating
communication between exemplary computing system 710 and one or
more additional devices. For example, in certain embodiments
communication interface 722 may facilitate communication between
computing system 710 and a private or public network including
additional computing systems. Examples of communication interface
722 include, without limitation, a wired network interface (such as
a network interface card), a wireless network interface (such as a
wireless network interface card), a modem, and any other suitable
interface. In at least one embodiment, communication interface 722
may provide a direct connection to a remote server via a direct
link to a network, such as the Internet. Communication interface
722 may also indirectly provide such a connection through, for
example, a local area network (such as an Ethernet network), a
personal area network, a telephone or cable network, a cellular
telephone connection, a satellite data connection, or any other
suitable connection.
In certain embodiments, communication interface 722 may also
represent a host adapter configured to facilitate communication
between computing system 710 and one or more additional network or
storage devices via an external bus or communications channel.
Examples of host adapters include, without limitation, SCSI host
adapters, USB host adapters, IEEE 1394 host adapters, SATA and
eSATA host adapters, ATA and PATA host adapters, Fibre Channel
interface adapters, Ethernet adapters, or the like. Communication
interface 722 may also allow computing system 710 to engage in
distributed or remote computing. For example, communication
interface 722 may receive instructions from a remote device or send
instructions to a remote device for execution. In certain
embodiments, communication interface 722 may perform and/or be a
means for performing, either alone or in combination with other
elements, one or more of the identifying, receiving, determining,
classifying, providing, analyzing, computing, iterating, selecting,
assigning, including, using, training, querying, and allowing steps
disclosed herein. Communication interface 722 may also be used to
perform and/or be a means for performing other steps and features
set forth in the instant disclosure.
As illustrated in FIG. 7, computing system 710 may also include at
least one display device 724 coupled to communication
infrastructure 712 via a display adapter 726. Display device 724
generally represents any type or form of device capable of visually
displaying information forwarded by display adapter 726. Similarly,
display adapter 726 generally represents any type or form of device
configured to forward graphics, text, and other data from
communication infrastructure 712 (or from a frame buffer, as known
in the art) for display on display device 724.
As illustrated in FIG. 7, exemplary computing system 710 may also
include at least one input device 728 coupled to communication
infrastructure 712 via an input interface 730. Input device 728
generally represents any type or form of input device capable of
providing input, either computer or human generated, to exemplary
computing system 710. Examples of input device 728 include, without
limitation, a keyboard, a pointing device, a speech recognition
device, or any other input device. In at least one embodiment,
input device 728 may perform and/or be a means for performing,
either alone or in combination with other elements, one or more of
the identifying, receiving, determining, classifying, providing,
analyzing, computing, iterating, selecting, assigning, including,
using, training, querying, and allowing steps disclosed herein.
Input device 728 may also be used to perform and/or be a means for
performing other steps and features set forth in the instant
disclosure.
As illustrated in FIG. 7, exemplary computing system 710 may also
include a primary storage device 732 and a backup storage device
733 coupled to communication infrastructure 712 via a storage
interface 734. Storage devices 732 and 733 generally represent any
type or form of storage device or medium capable of storing data
and/or other computer-readable instructions. For example, storage
devices 732 and 733 may be a magnetic disk drive (e.g., a so-called
hard drive), a floppy disk drive, a magnetic tape drive, an optical
disk drive, a flash drive, or the like. Storage interface 734
generally represents any type or form of interface or device for
transferring data between storage devices 732 and 733 and other
components of computing system 710. In one example, database 120
from FIG. 1 may be stored in primary storage device 732.
In certain embodiments, storage devices 732 and 733 may be
configured to read from and/or write to a removable storage unit
configured to store computer software, data, or other
computer-readable information. Examples of suitable removable
storage units include, without limitation, a floppy disk, a
magnetic tape, an optical disk, a flash memory device, or the like.
Storage devices 732 and 733 may also include other similar
structures or devices for allowing computer software, data, or
other computer-readable instructions to be loaded into computing
system 710. For example, storage devices 732 and 733 may be
configured to read and write software, data, or other
computer-readable information. Storage devices 732 and 733 may also
be a part of computing system 710 or may be a separate device
accessed through other interface systems.
In certain embodiments, storage devices 732 and 733 may be used,
for example, to perform and/or be a means for performing, either
alone or in combination with other elements, one or more of the
identifying, receiving, determining, classifying, providing,
analyzing, computing, iterating, selecting, assigning, including,
using, training, querying, and allowing steps disclosed herein.
Storage devices 732 and 733 may also be used to perform and/or be a
means for performing other steps and features set forth in the
instant disclosure.
Many other devices or subsystems may be connected to computing
system 710. Conversely, all of the components and devices
illustrated in FIG. 7 need not be present to practice the
embodiments described and/or illustrated herein. The devices and
subsystems referenced above may also be interconnected in different
ways from that shown in FIG. 7. Computing system 710 may also
employ any number of software, firmware, and/or hardware
configurations. For example, one or more of the exemplary
embodiments disclosed herein may be encoded as a computer program
(also referred to as computer software, software applications,
computer-readable instructions, or computer control logic) on a
computer-readable medium. The phrase "computer-readable medium"
generally refers to any form of device, carrier, or medium capable
of storing or carrying computer-readable instructions. Examples of
computer-readable media include, without limitation,
transmission-type media, such as carrier waves, and physical media,
such as magnetic-storage media (e.g., hard disk drives and floppy
disks), optical-storage media (e.g., CD- or DVD-ROMs),
electronic-storage media (e.g., solid-state drives and flash
media), and other distribution systems.
The computer-readable medium containing the computer program may be
loaded into computing system 710. All or a portion of the computer
program stored on the computer-readable medium may then be stored
in system memory 716 and/or various portions of storage devices 732
and 733. When executed by processor 714, a computer program loaded
into computing system 710 may cause processor 714 to perform and/or
be a means for performing the functions of one or more of the
exemplary embodiments described and/or illustrated herein.
Additionally or alternatively, one or more of the exemplary
embodiments described and/or illustrated herein may be implemented
in firmware and/or hardware. For example, computing system 710 may
be configured as an application specific integrated circuit (ASIC)
adapted to implement one or more of the exemplary embodiments
disclosed herein.
FIG. 8 is a block diagram of an exemplary network architecture 800
in which client systems 810, 820, and 830 and servers 840 and 845
may be coupled to a network 850. Client systems 810, 820, and 830
generally represent any type or form of computing device or system,
such as exemplary computing system 710 in FIG. 7.
Similarly, servers 840 and 845 generally represent computing
devices or systems, such as application servers or database
servers, configured to provide various database services and/or run
certain software applications. Network 850 generally represents any
telecommunication or computer network including, for example, an
intranet, a wide area network (WAN), a local area network (LAN), a
personal area network (PAN), or the Internet. In one example,
client systems 810, 820, and/or 830 and/or servers 840 and/or 845
may include system 100 from FIG. 1.
As illustrated in FIG. 8, one or more storage devices 860(1)-(N)
may be directly attached to server 840. Similarly, one or more
storage devices 870(1)-(N) may be directly attached to server 845.
Storage devices 860(1)-(N) and storage devices 870(1)-(N) generally
represent any type or form of storage device or medium capable of
storing data and/or other computer-readable instructions. In
certain embodiments, storage devices 860(1)-(N) and storage devices
870(1)-(N) may represent network-attached storage (NAS) devices
configured to communicate with servers 840 and 845 using various
protocols, such as NFS, SMB, or CIFS.
Servers 840 and 845 may also be connected to a storage area network
(SAN) fabric 880. SAN fabric 880 generally represents any type or
form of computer network or architecture capable of facilitating
communication between a plurality of storage devices. SAN fabric
880 may facilitate communication between servers 840 and 845 and a
plurality of storage devices 890(1)-(N) and/or an intelligent
storage array 895. SAN fabric 880 may also facilitate, via network
850 and servers 840 and 845, communication between client systems
810, 820, and 830 and storage devices 890(1)-(N) and/or intelligent
storage array 895 in such a manner that devices 890(1)-(N) and
array 895 appear as locally attached devices to client systems 810,
820, and 830. As with storage devices 860(1)-(N) and storage
devices 870(1)-(N), storage devices 890(1)-(N) and intelligent
storage array 895 generally represent any type or form of storage
device or medium capable of storing data and/or other
computer-readable instructions.
In certain embodiments, and with reference to exemplary computing
system 710 of FIG. 7, a communication interface, such as
communication interface 722 in FIG. 7, may be used to provide
connectivity between each client system 810, 820, and 830 and
network 850. Client systems 810, 820, and 830 may be able to access
information on server 840 or 845 using, for example, a web browser
or other client software. Such software may allow client systems
810, 820, and 830 to access data hosted by server 840, server 845,
storage devices 860(1)-(N), storage devices 870(1)-(N), storage
devices 890(1)-(N), or intelligent storage array 895. Although FIG.
8 depicts the use of a network (such as the Internet) for
exchanging data, the embodiments described and/or illustrated
herein are not limited to the Internet or any particular
network-based environment.
In at least one embodiment, all or a portion of one or more of the
exemplary embodiments disclosed herein may be encoded as a computer
program and loaded onto and executed by server 840, server 845,
storage devices 860(1)-(N), storage devices 870(1)-(N), storage
devices 890(1)-(N), intelligent storage array 895, or any
combination thereof. All or a portion of one or more of the
exemplary embodiments disclosed herein may also be encoded as a
computer program, stored in server 840, run by server 845, and
distributed to client systems 810, 820, and 830 over network 850.
Accordingly, network architecture 800 may perform and/or be a means
for performing, either alone or in combination with other elements,
one or more of the identifying, receiving, determining,
classifying, providing, analyzing, computing, iterating, selecting,
assigning, including, using, training, querying, and allowing steps
disclosed herein. Network architecture 800 may also be used to
perform and/or be a means for performing other steps and features
set forth in the instant disclosure.
As detailed above, computing system 710 and/or one or more
components of network architecture 800 may perform and/or be a
means for performing, either alone or in combination with other
elements, one or more steps of an exemplary method for classifying
unknown files based on user actions.
While the foregoing disclosure sets forth various embodiments using
specific block diagrams, flowcharts, and examples, each block
diagram component, flowchart step, operation, and/or component
described and/or illustrated herein may be implemented,
individually and/or collectively, using a wide range of hardware,
software, or firmware (or any combination thereof) configurations.
In addition, any disclosure of components contained within other
components should be considered exemplary in nature since many
other architectures can be implemented to achieve the same
functionality.
In some examples, all or a portion of exemplary system 100 in FIG.
1 may represent portions of a cloud-computing or network-based
environment. Cloud-computing environments may provide various
services and applications via the Internet. These cloud-based
services (e.g., software as a service, platform as a service,
infrastructure as a service, etc.) may be accessible through a web
browser or other remote interface. Various functions described
herein may be provided through a remote desktop environment or any
other cloud-based computing environment.
The process parameters and sequence of steps described and/or
illustrated herein are given by way of example only and can be
varied as desired. For example, while the steps illustrated and/or
described herein may be shown or discussed in a particular order,
these steps do not necessarily need to be performed in the order
illustrated or discussed. The various exemplary methods described
and/or illustrated herein may also omit one or more of the steps
described or illustrated herein or include additional steps in
addition to those disclosed.
While various embodiments have been described and/or illustrated
herein in the context of fully functional computing systems, one or
more of these exemplary embodiments may be distributed as a program
product in a variety of forms, regardless of the particular type of
computer-readable media used to actually carry out the
distribution. The embodiments disclosed herein may also be
implemented using software modules that perform certain tasks.
These software modules may include script, batch, or other
executable files that may be stored on a computer-readable storage
medium or in a computing system. In some embodiments, these
software modules may configure a computing system to perform one or
more of the exemplary embodiments disclosed herein.
In addition, one or more of the modules described herein may
transform data, physical devices, and/or representations of
physical devices from one form to another. For example, one or more
of modules 102 in FIG. 1 may transform a device (such as server 206
in FIG. 2) into a device capable of classifying unknown files based
at least in part on user actions.
The preceding description has been provided to enable others
skilled in the art to best utilize various aspects of the exemplary
embodiments disclosed herein. This exemplary description is not
intended to be exhaustive or to be limited to any precise form
disclosed. Many modifications and variations are possible without
departing from the spirit and scope of the instant disclosure. The
embodiments disclosed herein should be considered in all respects
illustrative and not restrictive. Reference should be made to the
appended claims and their equivalents in determining the scope of
the instant disclosure.
Unless otherwise noted, the terms "a" or "an," as used in the
specification and claims, are to be construed as meaning "at least
one of." In addition, for ease of use, the words "including" and
"having," as used in the specification and claims, are
interchangeable with and have the same meaning as the word
"comprising."
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